import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from warnings import filterwarnings
filterwarnings('ignore')

import statsmodels.api as sm
from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt

from sklearn.metrics import mean_squared_error, mean_absolute_error
import math

df = pd.read_excel('After Split.xlsx',date_parser=['Date'],index_col='Date')
df.head()

	Close Price
Date
2019-09-19	1101.05
2019-09-20	1199.60
2019-09-23	1257.25
2019-09-24	1253.80
2019-09-25	1239.70

df.isna().sum()

Close Price    31
dtype: int64

df.fillna(method='ffill',inplace=True)
df.isna().sum()

Close Price    0
dtype: int64

import plotly.express as pe
pe.box(df['Close Price'],points='all')

df.plot(figsize=(15,8),title='Close Price', fontsize=14)

<AxesSubplot:title={'center':'Close Price'}, xlabel='Date'>

train = df[:451]
test = df[451:]

train['Close Price'].plot(figsize=(15,8),title='Close Price', fontsize=14)
test['Close Price'].plot(figsize=(15,8),title='Close Price', fontsize=14)
plt.show()

Naive Method

dd = np.asarray(train['Close Price'])
y_hat = test.copy()
y_hat['naive'] = dd[len(dd)-1]

plt.figure(figsize=(12,8))
plt.plot(train.index, train['Close Price'], label='Train')
plt.plot(test.index,test['Close Price'], label='Test')
plt.plot(y_hat.index,y_hat['naive'], label='Naive Forecast')
plt.legend(loc='best')
plt.title("Naive Forecast")
plt.show()

mse_naive = mean_squared_error(test, y_hat.naive)
print('MSE: '+str(mse_naive))
mae_naive = mean_absolute_error(test, y_hat.naive)
print('MAE: '+str(mae_naive))
rmse_naive = math.sqrt(mean_squared_error(test, y_hat.naive))
print('RMSE: '+str(rmse_naive))

MSE: 6840.095719178087
MAE: 64.81164383561647
RMSE: 82.70487119377

Simple Average

y_hat_avg = test.copy()
y_hat_avg['avg_forecast'] = test['Close Price'].mean()

plt.figure(figsize=(12,8))
plt.plot(train['Close Price'], label='Train')
plt.plot(test['Close Price'], label='Test')
plt.plot(y_hat_avg['avg_forecast'], label='Average Forecast')
plt.legend(loc='best')
plt.title("Simple Average")
plt.show()

mse_sa = mean_squared_error(test, y_hat.naive)
print('MSE: '+str(mse_sa))
mae_sa = mean_absolute_error(test, y_hat.naive)
print('MAE: '+str(mae_sa))
rmse_sa = math.sqrt(mean_squared_error(test, y_hat.naive))
print('RMSE: '+str(rmse_sa))

MSE: 6840.095719178087
MAE: 64.81164383561647
RMSE: 82.70487119377

Moving Average

y_hat_avg = test.copy()
y_hat_avg['moving_avg_forecast'] = test['Close Price'].rolling(30).mean().iloc[-1]

plt.figure(figsize=(16,8))
plt.plot(train['Close Price'], label='Train')
plt.plot(test['Close Price'], label='Test')
plt.plot(y_hat_avg['moving_avg_forecast'], label='Moving Average Forecast')
plt.legend(loc='best')
plt.title("Moving Average Forecast")
plt.show()

mse_ma = mean_squared_error(test, y_hat_avg['moving_avg_forecast'])
print('MSE: '+str(mse_ma))
mae_ma = mean_absolute_error(test, y_hat_avg['moving_avg_forecast'])
print('MAE: '+str(mae_ma))
rmse_ma = math.sqrt(mean_squared_error(test, y_hat_avg['moving_avg_forecast']))
print('RMSE: '+str(rmse_ma))

MSE: 5903.774878234405
MAE: 59.33744292237448
RMSE: 76.83602591385375

Simple Exponential Smoothing

y_hat_avg = test.copy()
fit2 = SimpleExpSmoothing(np.asarray(train['Close Price'])).fit(smoothing_level=0.7,optimized=False)
y_hat_avg['SES'] = fit2.forecast(len(test))

plt.figure(figsize=(16,8))
plt.plot(train['Close Price'], label='Train')
plt.plot(test['Close Price'], label='Test')
plt.plot(y_hat_avg['SES'], label='SES')
plt.legend(loc='best')
plt.title("Simple Exponential Smoothing")
plt.savefig('SES.png')

mse_ses = mean_squared_error(test, y_hat_avg['SES'])
print('MSE: '+str(mse_ses))
mae_ses = mean_absolute_error(test,y_hat_avg['SES'])
print('MAE: '+str(mae_ses))
rmse_ses = math.sqrt(mean_squared_error(test, y_hat_avg['SES']))
print('RMSE: '+str(rmse_ses))

MSE: 6785.286521168236
MAE: 64.4897662050722
RMSE: 82.37285063155358

Holt’s Linear Trend method

sm.tsa.seasonal_decompose(train['Close Price'],period=30,model='additive').plot()
result = sm.tsa.stattools.adfuller(train['Close Price'])
plt.show()

result

(-0.8687373431296384,
 0.7980932839243056,
 6,
 444,
 {'1%': -3.4451642100030084,
  '5%': -2.8680716196949327,
  '10%': -2.5702491139112085},
 3989.7820701732276)

y_hat_avg = test.copy()

fit1 = Holt(np.asarray(train['Close Price'])).fit(smoothing_level = 0.3,smoothing_slope = 0.1)
y_hat_avg['Holt_linear'] = fit1.forecast(len(test))

plt.figure(figsize=(16,8))
plt.plot(train['Close Price'], label='Train')
plt.plot(test['Close Price'], label='Test')
plt.plot(y_hat_avg['Holt_linear'], label='Holt_linear')
plt.legend(loc='best')
plt.title("Holt's Linear Trend Method")
plt.savefig("HoltsLinear Method.png")

mse_hl = mean_squared_error(test, y_hat_avg['Holt_linear'])
print('MSE: '+str(mse_hl))
mae_hl = mean_absolute_error(test,y_hat_avg['Holt_linear'])
print('MAE: '+str(mae_hl))
rmse_hl = math.sqrt(mean_squared_error(test, y_hat_avg['Holt_linear']))
print('RMSE: '+str(rmse_hl))

MSE: 9027.790808752985
MAE: 68.75672141328316
RMSE: 95.01468733176458

Holt-Winters Method

y_hat_avg = test.copy()
fit1 = ExponentialSmoothing(np.asarray(train['Close Price']) ,seasonal_periods=30 ,trend='add', seasonal='add',).fit()
y_hat_avg['Holt_Winter'] = fit1.forecast(len(test))

plt.figure(figsize=(16,8))
plt.plot( train['Close Price'], label='Train')
plt.plot(test['Close Price'], label='Test')
plt.plot(y_hat_avg['Holt_Winter'], label='Holt_Winter')
plt.legend(loc='best')
plt.title("Holt-Winters Method")
plt.savefig('Holt-Winter.png')

mse_hw = mean_squared_error(test, y_hat_avg['Holt_Winter'])
print('MSE: '+str(mse_hw))
mae_hw= mean_absolute_error(test,y_hat_avg['Holt_Winter'])
print('MAE: '+str(mae_hw))
rmse_hw = math.sqrt(mean_squared_error(test, y_hat_avg['Holt_Winter']))
print('RMSE: '+str(rmse_hw))

MSE: 4386.615024881044
MAE: 53.02881742601117
RMSE: 66.23152591388065

Score Card

cols = ['Model_Name', 'MSE', 'RMSE', 'MAE']
Score_Card = pd.DataFrame(columns = cols)

naive = pd.Series({'Model_Name': "Naive Approach",
                     'MSE': mse_naive,
                     'RMSE': rmse_naive,
                     'MAE':mae_naive
                   })

Score_Card = Score_Card.append(naive,ignore_index=True)

sa = pd.Series({'Model_Name': "Simple Average",
                     'MSE': mse_sa,
                     'RMSE': rmse_sa,
                     'MAE':mae_sa
                   })

Score_Card = Score_Card.append(sa,ignore_index=True)

ma = pd.Series({'Model_Name': "Moving Average",
                     'MSE': mse_ma,
                     'RMSE': rmse_ma,
                     'MAE':mae_ma
                   })

Score_Card = Score_Card.append(ma,ignore_index=True)

ses = pd.Series({'Model_Name': "Simple Exponential Smoothing",
                     'MSE': mse_ses,
                     'RMSE': rmse_ses,
                     'MAE':mae_ses
                   })

Score_Card = Score_Card.append(ses,ignore_index=True)

hl = pd.Series({'Model_Name': "Holt’s Linear Trend method",
                     'MSE': mse_hl,
                     'RMSE': rmse_hl,
                     'MAE':mae_hl
                   })

Score_Card = Score_Card.append(hl,ignore_index=True)

hw = pd.Series({'Model_Name': "Holt’s Winter method",
                     'MSE': mse_hw,
                     'RMSE': rmse_hw,
                     'MAE':mae_hw
                   })

Score_Card = Score_Card.append(hw,ignore_index=True)

Score_Card.set_index('Model_Name',inplace=True)

Score_Card

	MSE	RMSE	MAE
Model_Name
Naive Approach	6840.095719	82.704871	64.811644
Simple Average	6840.095719	82.704871	64.811644
Moving Average	5903.774878	76.836026	59.337443
Simple Exponential Smoothing	6785.286521	82.372851	64.489766
Holt’s Linear Trend method	9027.790809	95.014687	68.756721
Holt’s Winter method	4386.615025	66.231526	53.028817